What are System Analyst metrics? Crafting the perfect System Analyst metrics can feel overwhelming, particularly when you're juggling daily responsibilities. That's why we've put together a collection of examples to spark your inspiration.
Copy these examples into your preferred app, or you can also use Tability to keep yourself accountable.
Find System Analyst metrics with AI While we have some examples available, it's likely that you'll have specific scenarios that aren't covered here. You can use our free AI metrics generator below to generate your own strategies.
Examples of System Analyst metrics and KPIs 1. Data Processing Throughput Measures the amount of data processed successfully within a given time frame, typically in gigabytes per second (GB/s)
What good looks like for this metric: Varies by system but often >1 GB/s for high-performing systems
Ideas to improve this metric Increase hardware capabilities Optimise software algorithms Implement data compression techniques Use parallel processing Upgrade network infrastructure 2. Latency Time taken from input to desired data processing action, measured in milliseconds (ms)
What good looks like for this metric: <100 ms for high-performing systems
Ideas to improve this metric Enhance server response time Minimise data travel distance Optimise application code Utilise content delivery networks Implement load balancers 3. Error Rate Percentage of errors during data processing compared to total operations, measured as a %
What good looks like for this metric: <5% for acceptable performance
Ideas to improve this metric Implement error-handling codes Train systems with more robust datasets Regularly update software Conduct thorough system testing Improve data input validity checks 4. Disk I/O Rate Measures read and write operations per second on storage devices, expressed in IOPS (input/output operations per second)
What good looks like for this metric: >10,000 IOPS for SSDs, lower for HDDs
Ideas to improve this metric Upgrade to faster storage solutions Redistribute data loads Increase cache sizes Use faster file systems Optimise database queries 5. Resource Utilisation Percentage of CPU, memory, and network bandwidth being used, expressed as a %
What good looks like for this metric: 75-85% for efficient resource use
Ideas to improve this metric Perform regular system monitoring Distribute workloads more evenly Implement scalable cloud solutions Prioritise critical processes Utilise virtualisation
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1. Job Success Rate Percentage of SQL Server jobs that complete successfully without errors during the specified window
What good looks like for this metric: Typically above 95%
Ideas to improve this metric Optimise SQL queries to reduce execution time Implement real-time monitoring and alerting Increase server capacity during the job window Regularly maintain and update indexes Perform routine job error analysis and debugging 2. Average Job Duration Average time taken by SQL jobs to complete within the window
What good looks like for this metric: Should align with historical average time
Ideas to improve this metric Refactor and optimise slow-performing queries Avoid unnecessary data processing Use SQL Server execution plans for analysis Schedule jobs in sequence to avoid performance bottlenecks Utilise parallel processing when possible 3. Data Availability Percentage of time that data is available and ready for use by end-users after job completion
What good looks like for this metric: Typically above 99%
Ideas to improve this metric Set up redundancy for critical tables Automate data validation checks post-job completion Implement failover strategies Ensure network reliability and minimise downtime Regularly back up and securely store data 4. Error Frequency Count of errors encountered during SQL job processing
What good looks like for this metric: Typically less than 5 errors per month
Ideas to improve this metric Conduct thorough testing before deployment Use transaction logs to identify error sources Ensure up-to-date error handling mechanisms Regularly review job logs for anomalies Provide regular training for administrators 5. Resource Utilisation Percentage of server resources used during job processing
What good looks like for this metric: Should not consistently exceed 70%
Ideas to improve this metric Balance load across multiple servers Monitor and adjust resource allocation Upgrade hardware capacity if needed Eliminate unused processes during job execution Use performance counters to track and adjust load
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Tracking your System Analyst metrics Having a plan is one thing, sticking to it is another.
Setting good strategies is only the first challenge. The hard part is to avoid distractions and make sure that you commit to the plan. A simple weekly ritual will greatly increase the chances of success.
A tool like Tability can also help you by combining AI and goal-setting to keep you on track.
More metrics recently published We have more examples to help you below.
Planning resources OKRs are a great way to translate strategies into measurable goals. Here are a list of resources to help you adopt the OKR framework:
Tability's check-ins will save you hours and increase transparency
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